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At last week’s Legalweek show. in New York City, one of the salient themes was how law firms and corporate legal departments are preparing for the future, developing tools and techniques for operational efficiencies and effectiveness. In his keynote, Andrew McAfee, Visionary Futurist & Director of the MIT Initiative of the Digital Economy, highlighted how organizations can survive and thrive in the years to come (specifically, 10) as robots, drones, artificial intelligence and like transform conventional business practices to advance everyday processes for organizations, their employees and constituents.

If, in fact, these technologies are simply warm-up acts for what is to come, here’s our take on what the not-so-distant future holds (specifically, now). The core problem forming the basis of our prediction is age-old—that is, the need for corporations to save costs associated with eDiscovery while minimizing current and future risk. However, the approach is new: an emerging predictive analytics trend shifting to a future focus.

What is Big Data and Why is It a Problem?

The term “big data” started life as a way to describe massive data volumes in disparate locations. Since then people refined the term to not only mean discrete large data sets, but the process of aggregating these volumes into a single repository for visibility and insight. Developers built big data technologies primarily for data storage management and business intelligence customers.

In the legal world, big data refers to massive data sets that are hosted across a variety of eDiscovery platforms and data sources. The sheer amount of potentially relevant data makes it hard to collect and review within acceptable timelines, without spending astronomical amounts on human and machine resources.

Another dimension to the big data problem is that corporations often have several law firms and litigation support companies working for them, each with their own eDiscovery platform, processes, and individual work product. The consequence is that it’s difficult, if not near-impossible, for multiple reviewers working on a single matter to communicate with one another; there is no centralized data vault for all reviewers to share work product; there’s no way to communicate learning and previous review across multiple matters.

Why Basic Analytics are Inadequate for Big Data Jobs

Until very recently, the target of analytics focused on newer tools such as technology assisted review (TAR), concept searching, relationship analysis, email threading, dedupe and near-dupe, data clustering and visualization, and other basic text analytics (not to mention our old friend keyword searching). While there are measurable benefits., these traditional approaches are limited to single matters, and also may or may not be able to analyze chat records, IMs, or social media postings for critical information like non-compliance, even though bad actors frequently use these messaging platforms.

The Age of Predictive and Prescriptive Big Data Analytics

Adding big data analytics strategies. that are both predictive and prescriptive to the mix means companies can make better use of their data across all cases, vendors and review platforms to achieve greater business benefits. This model ingests data across multiple platforms, data types and third-party vendors into a central repository. It applies advanced analytics and enables the application of up to billions of previous designations to current matters. It also predicts potential problems with ingested data. The more matters that you include in the data repository, the more intelligent the analytics engine becomes — saving you time, resources, risk, and cost. Here’s a closer look how incorporating a big data analytics strategy. is helping legal departments survive and thrive now and in the years to come:

Reuse attorney work product. Advanced analytics apply machine learning and attorney work product across subsequent matters. Repurposing attorney work product results in higher ROI by incorporating critical insights. (In other words, no reinventing the wheel with each new matter.)

Grant new and ongoing insights into your data. Big data analytics analyzes and culls data sets working in conjunction with functions like TAR, concept mapping, categorization, actionable visualization, and dedupe/near-dedupe. It also identifies relevant documents for new cases based on previous ones, and can predict which documents in the consolidated repository may present compliance risk or legal liability.

Protect data. Big data analytics enables legal teams to detect inconsistently classified documents, mitigating risk. Legal teams often engage in costly repeat review of the same documents, which can lead to coding or classifying documents differently each time. By flagging inconsistent coding, the Analytics Hub prevents overlooking relevant documents and exposing private or other sensitive data.

Experts Round out the Big Data Picture

You need not go it alone, however. In fact, data scientists are fast becoming head hunters’ best friends. Many organizations are hiring internally by business line, but this self-service model isn’t always practical. Instead, many legal departments are opting to bring in third-party experts to leverage big data technology, customize algorithms to suit your specific needs, and provide legal process and subject matter expertise. As technological advances marshal eDiscovery into a new era, big data analytics strategies to take center stage.